ASTRA — Unified Research Lab + MCP Server

Server Quality Checklist

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Latest release: v2.8.0
Disambiguation4/5
Most tools have distinct, specific purposes due to domain-specific prefixes and detailed descriptions. However, the large number (50) and some acronym-heavy names may cause minor confusion between closely related tools like tcai_cycle and tcai_active_inference.
Naming Consistency3/5
Naming is somewhat inconsistent: subdomain prefixes (tcai_, np_, snn_, wm_, sensor_) provide structure, but verb placement varies (e.g., `inject_spikes` vs `export_snapshot` vs `simulation_control`). Some tools use `get_` prefix while others don't, creating a mix.
Tool Count2/5
With 50 tools, the server is overloaded relative to typical MCP servers. While the scope is broad (consciousness, neuroplatform, world model, sensors, SNN), the number of tools makes navigation and selection difficult for an agent.
Completeness4/5
The tool surface covers core workflows for each subdomain: consciousness cycle, neuroplatform control, world model operations, sensor fusion, and ethical checks. Minor gaps exist (e.g., no explicit tool for configuration persistence), but overall it is comprehensive.
Average 2.7/5 across 50 of 50 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but fails entirely. It does not disclose whether the operation is destructive, reversible, requires authentication, or has side effects on the system state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While extremely short, 'Spike Injection' is under-informative rather than concise. It wastes the opportunity to provide meaningful content and is not structured to front-load critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 parameters, no output schema, and no annotations, the two-word description is completely insufficient. The agent cannot determine how to use the tool appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning beyond the schema. The two parameters (strength and neuronIds) are not explained in context; for example, what 'strength' means or which neurons are targeted. Schema description coverage is 0%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Spike Injection' is a tautology that merely restates the tool name 'inject_spikes' without specifying what injecting spikes means or what the tool accomplishes. It provides no actionable information for an AI agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus any of its 47 siblings such as np_send_trigger or np_count_spikes. There is no mention of prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description discloses no behavioral traits such as side effects, permissions, or computational cost. It is completely opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short but ineffective. Conciseness is misused as under-specification. Every word should add value; here it does not.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no description of return values. The tool is completely undefined in terms of what the agent receives. In context of complex siblings, this is severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema coverage 100% due to empty schema), but the description adds no meaning. It fails to explain what the tool returns or how it operates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Consciousness Assessment (Proxy)' is a noun phrase that vaguely indicates the tool assesses consciousness, but lacks a clear verb and resource specification. It does not differentiate from siblings like 'get_system_status' or 'get_metrics'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Among numerous sibling tools for assessment and sensing, no context is provided for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It does not disclose any behavioral traits such as whether the tool has side effects, is read-only, requires permissions, or what the output format is. This is a critical gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (3 words) but this is under-specification rather than conciseness. It lacks essential information and does not 'earn its place' by providing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (fusing multiple sensor modalities), the lack of annotations, no output schema, and 3 boolean parameters, the description is woefully incomplete. An agent cannot infer the tool's behavior, inputs, or outputs from this description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning no parameter descriptions exist in the schema. The description does not add any meaning beyond the parameter names (includeAudio, includeVisual, includeOlfactory). For a tool with 3 parameters, the description should explain how these booleans affect fusion behavior, but it does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Cross-Modal Attention Fusion' is a noun phrase, not a clear verb+resource statement. It does not specify what the tool does (e.g., fuses sensor data, computes attention, etc.). Compared to sibling tools like sensor_audio or sensor_visual which are self-explanatory, this description is vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives like sensor_audio, sensor_olfactory, or other fusion-related tools. An agent would have no basis for deciding to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description offers no behavioral information such as side effects, read-only nature, or access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but at the expense of clarity. It is under-specification rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and many sibling tools, the description is completely inadequate. It provides no information on what the tool returns or how it differs from similar tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description does not need to elaborate on them. The baseline for zero parameters is 4, and the description does not contradict this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Bio-Computing Platforms' is vague and does not convey the tool's function. It fails to state a verb or resource, and does not align clearly with the tool name 'get_platform_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage context or guidance is provided. The description does not specify when to use this tool versus any of its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits. However, it is entirely silent on side effects, permissions, or any operational characteristics, leaving the agent uninformed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but provides no useful information. It is under-specified rather than concise, failing to earn its place with meaningful content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, and the presence of many sibling status tools, this minimal description is entirely insufficient for an agent to understand the tool's purpose and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the baseline is 4. The description does not need to add parameter information since there are none, and it does not detract from this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'ASTRA System Status' is a tautology of the tool name, providing no verb or action to clarify what the tool does. It fails to indicate that it retrieves or displays system status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like get_platform_status or sensor_status. The description lacks any context about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses no behavioral traits: no mention of side effects, return values, or permissions. The tool remains a black box.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is too short to be useful. It consists of a single unclear phrase. While concise, it does not earn its place because it lacks clarity and utility.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description should still explain what the tool does. It fails to provide any meaningful information, making it completely inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0 parameters with 100% coverage. Baseline is 3, but the description adds no meaning beyond the schema and is confusing. It fails to clarify the tool's purpose, so it does not add value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'IRB Neural Welfare Check' is vague and fails to clearly state what the tool does. It does not use a specific verb to describe the action, and it is ambiguous whether it performs an ethical review or a welfare check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool versus sibling tools, nor does it specify prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a two-word description, the tool's behavior is completely opaque. Whether it is read-only, its side effects, or required permissions are undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely brief but lacks substance; it is under-specified rather than concise. The description could be improved with a single clear action verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should elaborate on the returned state. It fails to provide a complete picture for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters and schema coverage is 100%, baseline is 3. The description does not add any meaningful context beyond what the schema already shows.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'SNN Engine State' barely restates the tool name, offering little clarity on its specific purpose. It does not explicitly state that the tool retrieves or returns the state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like snn_step or snn_reset. The context and prerequisites are entirely absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description does not disclose behavioral traits such as side effects, required permissions, or reversibility. As a mutation tool, more transparency is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
At three words, the description is severely under-specified. Conciseness is not achieved at the expense of necessary detail; the tool warrants more explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the schema (2 required params) and lack of annotations or output schema, the description fails to provide essential context about the parameter space, range, or effect, leaving agents unable to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The description adds no meaning beyond the parameter names 'path' and 'value', leaving their semantics entirely unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Modify State Parameter' is vague and largely tautological with the tool name 'set_parameter'. It does not specify what state or parameter is being modified, making it difficult to distinguish from other tools that might modify parameters (e.g., np_configure_stim).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools that may set parameters (e.g., np_configure_stim, tcai_calibrate), the description lacks context for appropriate usage or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description alone must disclose behavioral traits. It says nothing about side effects, permissions, destructive behavior, or operational impact. The label gives no behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short (3 words) but at the cost of substance. It is under-specified rather than concise; the space is not used effectively to convey necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested parameter, no output schema, many siblings), the description is completely inadequate. It omits return behavior, trigger conditions, and integration context, making it nearly useless for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds no meaning to the parameters. It does not explain how the nested 'action' properties (targetNeurons, strengths, duration) relate to violation-of-expectation detection. The agent learns nothing beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description is a 3-word label ('Violation-of-Expectation Detection') that vaguely hints at purpose but lacks a verb or resource. It does not specify what the tool does (e.g., compute, detect, or return) and fails to distinguish it from siblings like wm_predict or tcai_curiosity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Without context on preconditions, expected input scenarios, or when not to use it, the agent cannot effectively select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for disclosing behavioral traits, but it says nothing about side effects, latency, or the meaning of 'real-time'. The tool may be read-only, but this is not stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (two words), but it sacrifices clarity and usefulness. Conciseness without adequate information is under-specification, not efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no parameter details, the description fails to provide enough context for an agent to understand what metrics are returned or how to interpret the output. It is insufficient for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so schema coverage is 100% trivially. The description adds no parameter-specific information, but the baseline for zero-parameter tools is 4, as no additional parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Real-time Metrics' is vague and does not specify what type of metrics (system, neural, performance, etc.) are returned. It barely clarifies beyond the tool name and does not distinguish it from sibling like 'tcai_metrics'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as side effects, required permissions, or the lifecycle of simulation states. The enum values hint at behaviors but are not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (two words) but lacks substance. It is under-specified rather than concise, omitting critical information that the agent needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has only one parameter with an enum and no output schema, the description should explain the effects of each command. It fails to provide sufficient context for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no information about the 'command' parameter. The enum values are standardized but the description does not elaborate on their meanings or expected outcomes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description repeats the tool name ('Simulation Control') without specifying its actions or distinguishing it from sibling tools. The purpose is vaguely implied but not explicitly stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description lacks context for when to start, stop, or check status, and does not differentiate from other control or status tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It fails to state whether the tool is read-only, destructive, requires authorization, or any side effects. The one-phrase description gives no behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single phrase, which is too short for this tool's complexity (nested object parameter, multiple constraints). It sacrifices substance for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameter, no output schema, and no annotations, the description is entirely insufficient. It does not explain return values, planning process, or relation to spike injection, leaving an agent with no useful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add any extra meaning to the 'goal' or 'horizon' parameters beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'CEM Planning for Optimal Spike Injection' vaguely indicates planning for spike injection, but 'CEM' and 'Optimal Spike Injection' are unclear jargon. It does not distinguish from sibling tools like wm_predict or wm_encode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description provides no context about prerequisites, when not to use, or how it fits into a workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose whether the tool modifies state, requires a specific world model state, or is safe to call multiple times. The description is too brief.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single vague phrase. While concise, it is under-specified and fails to convey necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameter, no output schema, and many sibling tools, the description is completely inadequate. It provides no explanation of what the training step accomplishes, what the response looks like, or how it fits with other tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add any additional context about the 'action' parameter beyond the schema's own descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it's a training step for the world model, but is vague. It does not specify that it applies an action and updates the model, nor does it distinguish from sibling tools like wm_encode or wm_predict.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives. The description does not mention prerequisites or context for training.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description gives no behavioral details such as impact, format, or side effects of exporting a snapshot.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three words are too brief to convey meaningful information; conciseness is achieved at the expense of clarity and utility.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no description of return values; agent cannot infer what data the snapshot contains or how to use the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0 parameters (100% coverage), so no parameter explanation is needed; description is sufficient for the schema's simplicity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Full State Snapshot' is a tautology of the tool name 'export_snapshot', providing no additional clarity on what state is captured or how it differs from sibling tools like tcai_workspace_state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives; context signals show many sibling tools with overlapping purposes, but description offers no differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides no behavioral information. With no annotations, the burden is on the description to disclose traits like read-only, side effects, or return structure. Nothing is conveyed, so transparency is nonexistent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short but incomplete as a sentence. It lacks a verb and reads more like a title. While concise, it sacrifices clarity by not being a full imperative statement. Could be improved with minimal addition like 'Get world model status and metrics.'
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (no parameters, no output schema), the description is still insufficient. It does not explain what metrics are included or what 'status' entails. The agent lacks context to understand the tool's output or value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the baseline is 4. The description does not add any parameter-specific meaning, but since there are no parameters, no additional information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'World Model Status & Metrics' is essentially a noun phrase that restates the tool name without a verb indicating action. It lacks specificity on what the tool does (e.g., retrieve, get), making it vague and not distinguishing it from similar sibling tools like get_system_status or get_metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not indicate when to use this tool versus alternatives, nor does it mention prerequisites or context. This leaves the agent without direction for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden of behavioral disclosure. It only mentions encoding but does not reveal side effects, permissions, or whether it is read-only or mutating. This is a significant gap for a processing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single phrase, which is concise but lacks structure. It is front-loaded with the tool's identity but omits essential details, making it minimally acceptable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested parameters, no output schema, many siblings), the description is incomplete. It does not explain the return format, the effect of the simulate parameter, or how image vs. video affects output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds minimal value by hinting 'Image/Video' which correlates with the videoFrames parameter, but does not explain the role of the input sub-parameters like simulate or source.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'V-JEPA 2 Visual Encoding (Image/Video)' indicates the tool performs visual encoding using a specific model, but the verb 'encoding' is somewhat vague and does not clearly state the action (e.g., 'encode an image or video'). It distinguishes from sibling sensor tools like sensor_audio but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling sensor tools (e.g., sensor_fuse, sensor_process), explicit usage context or conditions are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'Advance', which implies a forward time step, but does not clarify side effects (e.g., resets or triggers), required permissions, or whether the simulation state is mutated permanently.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short, which might be efficient if complete, but it omits critical context. It is under-specified rather than concise; key information about parameters and behavior is missing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple single-parameter schema and no output schema, the description should cover at least the effect of advancing and the role of 'steps'. It does not, leaving gaps about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter 'steps' with constraints, but the description does not mention it. Schema description coverage is 0%, so the description adds no meaning beyond what the schema already provides, which is a missed opportunity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advance SNN Simulation' clearly indicates the action and resource, but it lacks specificity. It does not distinguish this tool from siblings like snn_reset or simulation_control, leaving the agent unclear about what 'advance' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidelines are provided about when to use this tool versus alternatives. The description does not mention prerequisites, typical use cases, or suggest other tools like simulation_control for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description does not disclose side effects, authorization needs, or confirm read-only nature. The term 'Query' implies safety but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely brief, but at the cost of completeness. The single sentence conveys basic purpose but omits crucial details, making it under-specified rather than efficiently concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and moderate complexity, the description fails to explain what the query returns, how it handles edge cases (e.g., empty results), or any limitations. It lacks completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only 33% of parameters have schema descriptions (window_sec), and the tool description adds no parameter information. For a 3-parameter tool with low coverage, the description should explain each parameter's role; it does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Query') and resource ('SpikeEvent: individual spike timings over a window'), distinguishing it from sibling count tools like np_count_spikes. However, it lacks specificity on what exactly is returned (e.g., list of timestamps per channel).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings (e.g., np_count_spikes vs. spike events). No explicit conditions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description alone must convey behavioral traits. It does not disclose whether the tool is read-only, what it returns, or any side effects. The minimal text does not compensate for the lack of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (7 words), which is concise but not well-structured. It is a fragment that could be expanded with key information without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (no parameters, no output schema), the description should clarify what status information is provided. It fails to explain the output or distinguish from similar sibling tools, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema coverage is 100% by default. According to guidelines, baseline is 4. The description adds context by mentioning 'Platform & Controller Status,' which hints at the nature of the output, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'NeuroPlatform v2 — Platform & Controller Status' lacks a verb and does not state what action the tool performs. It vaguely indicates a status check but is more of a title than a clear purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool, when not to, or how it contrasts with siblings like get_platform_status. The description provides no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as read-only status, return data format, permissions, or side effects. For a status tool, the agent needs to know if it is safe to call, but the description adds no transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (4 words) and concise, but it is under-specified, lacking structure such as sentences or bullet points. It does not earn its place by providing all necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, no output schema, and no annotations, the description is too brief to be complete. It does not explain what 'status' entails, the return value, or any observable effects, leaving a significant gap for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero properties, so schema description coverage is 100%. Per the rubric, 0 parameters baseline is 4. The description does not add meaning beyond the schema, but the baseline holds.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Multimodal Sensor Pipeline Status' indicates the tool returns status but lacks a specific verb (e.g., 'get') and does not clarify whether it is a health check, configuration summary, or active pipeline state. It is a noun phrase, making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like sensor_audio or get_system_status. There are no explicit context, when-not-to-use, or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits like side effects or state changes. The mention of 'inertia' hints at temporal smoothing but is not explained. No information about read-only or destructive nature is given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: one sentence with no wasted words. However, it omits critical details, so conciseness comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 4 parameters, no output schema, and no annotations, the description fails to explain expected inputs, outputs, or behavior. It is insufficient for an agent to correctly invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description must explain parameters. It only says 'raw signals' without linking to the four parameters (threat, novelty, rewardSignal, controllability), leaving their semantics entirely unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool appraises raw signals into PAD emotional space (Mehrabian) with inertia. The verb 'appraise' and specific reference to a psychological model differentiate it from generic tools, though it does not explicitly distinguish from sibling tools like tcai_active_inference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor are prerequisites or context mentioned. The agent is left to infer usage from the name and high-level purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose whether the tool is read-only, destructive, requires authentication, or any side effects. The term 'capture' could mislead agents into thinking it creates a capture, while it likely retrieves data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (one phrase), but it is not verbose. However, it is too terse to be informative, earning a 3.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter and no output schema/annotations, the description fails to explain what 'descriptor + viability' means or what the return value is, making it incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'mea', described as 'MEA selector [1-5]'. The description adds no further meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Last MEA camera capture (descriptor + viability)', which indicates retrieving the most recent capture from an MEA camera, but the terms 'descriptor' and 'viability' are vague. It does not differentiate from sibling tools like np_status or sensor_visual.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No when-to-use or when-not-to-use conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description fails to disclose side effects, resource usage, or state changes. It only states it is a 'pipeline' but does not explain what happens during execution or what the output represents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and concise, but the phrase 'Fused z' may be ambiguous. It could be clearer without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description should compensate but fails to explain return values, behavior when parameters are omitted, or relationship to sibling sensor tools. It is insufficient for a complex pipeline tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions, so the description adds no additional meaning. Baseline score of 3 applies as the schema provides sufficient documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Full Multimodal Pipeline (All Modalities → Fused z)' indicates it processes multiple sensor modalities and produces a fused output. However, it lacks a specific verb like 'process' or 'fuse', and does not clearly distinguish from sibling tools like sensor_fuse or individual sensors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as sensor_audio, sensor_olfactory, or sensor_fuse. The description does not mention conditions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but only states 'Reset SNN Engine', omitting details like whether it clears spikes, weights, or state, and whether it is destructive or reversible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise but under-specified. While brevity is valued, the single phrase does not provide sufficient information for an agent to use the tool correctly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and no parameter descriptions, the description fails to convey the tool's purpose, effects, or prerequisites. It is inadequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the baseline is 4 per instructions. The description does not add any parameter semantics since none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Reset SNN Engine' clearly specifies the action (Reset) and the target resource (SNN Engine), distinguishing it from sibling tools like snn_step or inject_spikes. However, it lacks any elaboration on scope or effect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like tcai_reset or other reset operations. The description provides no context for choosing snn_reset over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as read-only nature, authentication needs, or any side effects. The minimal description fails to inform the agent about what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one sentence), but it lacks structure and omits important details that could be included without significant length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description does not provide enough context about the output format, behavior, or use cases; it feels incomplete for a query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for both parameters (dedup, window_sec), so the description adds no additional meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool queries triggers sent to an organoid over a time window, but does not differentiate it from sibling tools like np_send_trigger or np_query_spike_count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives; the description lacks any context about prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose whether the tool is read-only, modifies state, or has side effects. For a sensor tool, it should indicate if it consumes real hardware or simulates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short line, with no wasted words. However, it sacrifices substance for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complex input schema with 6 nested parameters and no output schema, the description is insufficient. It does not explain return values, behavior, or how to interpret outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, so the schema itself documents parameters. The description adds only a generic 'Olfactory sensor parameters' line, providing no extra meaning beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)' suggests the tool encodes olfactory stimuli into a latent representation, but lacks a clear verb+resource. It does not distinguish from sibling sensors like sensor_audio or sensor_visual.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. With sibling tools like sensor_audio, sensor_visual, and tcai_*, the description provides no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose read-only/destructive nature, auth needs, side effects, or output behavior. Fails to carry transparency burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single short sentence with minimal waste, but lacks a verb and could be more informative without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-param tool, description lists topics but omits what the tool returns, whether it mutates state, or how it interacts with the system. Incomplete for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters; schema coverage is 100% trivially. Description adds context on the tool's content (interoception, etc.), which is useful since the schema is empty.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description names the resource ('self-representation state') and lists aspects but lacks a verb indicating action. It's vague whether this is a getter, setter, or state machine.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like tcai_workspace_state or tcai_metaconsciousness. No exclusions or alternatives given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description must fully disclose behavioral traits. It only states the purpose, failing to mention side effects, safety (e.g., reads vs writes), return characteristics, or dependency on prior state. This is insufficient for an agent to understand implications of invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (4 words, no sentence structure). While concise, it lacks a proper sentence (starting with a verb in imperative form would be better). It is not verbose, but the terseness reduces clarity and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and the description does not explain what the prediction result contains (e.g., state representation, probability distribution). Given complexity (world model prediction) and many sibling tools, the description is too sparse to enable correct invocation and interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all parameters (action and steps), including nested fields. The description adds no additional meaning beyond the schema, so it meets the baseline. It does not clarify format or constraints further, which is acceptable given schema completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action of predicting the next SNN state in latent space, which is a specific verb and resource. However, it does not mention that the prediction is conditioned on an action input (visible in schema), nor does it explicitly distinguish from sibling tools like wm_plan or wm_encode, but the distinct resource term 'Next SNN State' differentiates somewhat.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., wm_plan, wm_encode). The description does not indicate prerequisites, typical inputs, or when not to use it, leaving the agent without decision support for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It mentions coupling to ethics and driving the SNN, implying state-modifying side effects, but does not disclose specifics like whether organoid state is altered, what the coupling entails, or if the tool has destructive consequences. More detail is needed for a safe agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently communicates the core function. It is front-loaded with the main purpose. However, it could be slightly more structured (e.g., list key components) but remains concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and moderate complexity (4 parameters, closed-loop coupling), the description lacks completeness. It does not explain return values, error conditions, prerequisites, or side effects. Given the absence of annotations, more context is necessary for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75% (three of four parameters have descriptions). The tool description does not add meaning beyond the schema; it repeats the high-level concept. The parameter descriptions in schema are adequate for drive_snn, couple_ethics, and drive_strength, but window_ms lacks a description. Baseline 3 is appropriate given high coverage without additional enrichment.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a closed-loop operation that reads from an organoid and couples to ASTRA fusion/ROS/ethics, with optional SNN driving. The verb 'read' and 'couple' specify the action, and it distinguishes from sibling tools like snn_step or inject_spikes by emphasizing the closed-loop integration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives such as snn_step, inject_spikes, or check_ethics. The description implies it is for closed-loop experiments but does not state prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden but only gives a basic functional description. It lacks details on side effects, permissions, or data handling, which is insufficient for a tool that may operate on platform state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise, but the phrasing 'Closed-loop _count_spike' is somewhat awkward and could be clearer. Still, front-loaded and no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing output schema and the description does not explain return format (e.g., list of electrode IDs and counts). For a simple tool, more detail on output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents both parameters. The description adds no extra meaning beyond what's in the schema, hence baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool counts spikes per electrode over a time window, linking to NeuroPlatform v2 and closed-loop context. However, it does not differentiate from similar sibling tools like np_query_spike_count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implies a closed-loop use case but does not provide when-not or alternative suggestions, which is a gap given many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It only hints at processing steps but does not reveal whether the tool captures live audio (hardware requirement), can run offline, what side effects occur (e.g., file creation), or if it is destructive. The pipeline mention adds minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (6 words) and front-loaded. Every word earns its place, but the brevity sacrifices clarity—it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (nested parameters, no annotations, no output schema), the description is incomplete. It lacks high-level context such as how the sensor data is sourced (real microphone vs simulation), the nature of the output (latent embedding?), or how it relates to other sensor tools. Schema coverage compensates for parameters but not for overall tool understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already explains each nested parameter. The tool description adds no extra semantic value beyond the schema. Baseline 3 is appropriate as the schema is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates a specific processing pipeline (Waveform→Mel→Latent) for audio encoding. It uses a specific verb 'Encoding' and resource 'Audio'. However, it does not explicitly differentiate from sibling sensors like sensor_visual or sensor_olfactory beyond the tool name, missing an opportunity to clarify its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not state when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. For a sensor tool, guidance on real vs simulated audio or integration with other modules would be valuable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only mentions the retrieval method. It does not specify side effects (e.g., read-only), performance, or prerequisites for use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence without fluff. However, it could be slightly expanded to improve clarity without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, blending behavior, or how to interpret results, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 80% (topK missing description), the tool description adds no parameter information. It fails to explain how parameters like 'embedding' or 'arousal' relate to the retrieval method.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve'), the resource ('memories'), and the method ('blended cosine similarity, PAD congruence and salience'), distinguishing it from sibling tools like tcai_memory_store.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives, such as get_acm_score, or when not to use it. The agent receives no context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It lists included state components but discloses no behavioral traits such as read-only nature, side effects, or destructive potential. The agent gains little understanding of the tool's operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence, making it concise. However, it is a list without a verb, which could be improved for clarity. Still, it avoids unnecessary words and is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lists relevant state components but does not explain their meanings, formats, or how to interpret the output. Given the complexity of the cognitive architecture domain and the absence of an output schema, more detail is needed for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so no parameter documentation is needed. The description adds no parameter information, but with 100% schema coverage and no parameters, the baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides 'Global Neuronal Workspace state' listing several sub-states (ignition, focus, qualia, etc.), which indicates what it does. However, it lacks a verb and does not differentiate from siblings like tcai_metrics or tcai_self_model that might also return state information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not specify when to use this tool versus alternatives or mention any prerequisites or exclusions, which is insufficient given the large set of sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the computation algorithmically but does not disclose behavioral traits like side effects, resource usage, or state modifications. Without annotations, the description should clarify read-only or safe behavior; it only mentions defaults.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph that front-loads purpose and mechanism efficiently, though technical jargon may reduce readability. No wasted sentences, but could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema or annotation, and the description omits what the tool returns (e.g., a numeric value or structure). Also lacks mention of side effects or performance considerations, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers the sole parameter with description including default. The tool description repeats that default information, adding no new meaning beyond schema, keeping at baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it computes an intrinsic reward/curiosity signal via prediction error, with specific mechanism (RNDCuriosity port). Distinguishes from siblings like tcai_metrics or tcai_emotion_appraise by focusing on novelty-driven exploration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives among many tcai tools. The description implies use for exploration but does not specify exclusions or comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It mentions "attention-gated, salience-indexed" but does not explain what these mean for behavior, side effects, duplicates, or return values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no fluff. While short, it is efficiently structured. Could benefit from a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description omits critical contextual details: what happens after storage, prerequisites, how attention/salience affect storage, and any side effects. For a storage tool with 6 parameters and no output schema, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 83%, so the schema already documents parameters. The description adds no extra parameter context beyond what the schema provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb-resource pair ("Store an experience in emotional memory") and adds distinguishing qualifiers ("attention-gated, salience-indexed") that differentiate it from siblings like tcai_memory_retrieve.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies storing experiences but gives no explicit guidance on when to use, when not, or alternatives. Sibling tools include other memory and cognitive tools, but no usage context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It states it queries 'spikes/minute per electrode over a time window', implying a read-only aggregation, but does not confirm idempotency or side effects. Minimal behavioral context beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that conveys the core function without extraneous words. It is front-loaded with the key purpose, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and three parameters, the description does not explain return value format, pagination, error handling, or behavior for missing parameters. It is incomplete for a query tool that might produce substantial results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 of 3 parameters described). The tool description adds no additional meaning beyond the schema's descriptions for fsname and window_sec, and does not compensate for the undocumented nonzero_only parameter. Parameter semantics are lacking.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries spike count (spikes/minute per electrode) over a time window, using specific verb 'query' and resource. It is distinguishable from siblings like np_query_spike_events and np_count_spikes by mentioning aggregation over time.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, nor does it reference sibling tools for comparison. The context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavior fully. It describes outputs but does not state whether the tool is read-only, modifies state, or requires specific prerequisites. The description lacks clarity on side effects or computational cost.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with key information front-loaded ('Longitudinal developmental tracking'). It is dense but not verbose, though the parenthetical could be slightly restructured for easier parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with no output schema, the description adequately explains what the tool tracks and returns. However, additional context on how to interpret the stages and scores would improve completeness for agents unfamiliar with the domain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and it compensates by describing the function adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as performing longitudinal developmental tracking, listing specific outputs (coarse stage, stability, meta-representation score) and mentions second-order self-monitoring. It distinguishes itself from sibling tools that focus on other cognitive functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like tcai_self_model or tcai_metaconsciousness. The agent is left to infer usage context from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must convey behavioral traits. It mentions optional RPE injection, suggesting potential state modification, but does not clarify if the tool is read-only or if injecting updates internal state. The output behavior is partially described but side effects are ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: three sentences, no filler, front-loaded with the main concept and key conditions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks an explicit mention of the return format or structure, despite having no output schema. The user must infer that velocity and noveltySpike are returned, but no details on data type or how to interpret them beyond the given conditions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'rpe'. The description adds only the phrase 'Optionally inject an RPE sample', which does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides meta-learning state, specifically learning velocity and noveltySpike from RPE-variance dynamics. It distinguishes itself from siblings like tcai_curiosity by focusing on meta-learning state, but could be more explicit about the output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus siblings such as tcai_second_order or tcai_calibrate. The description implies usage for monitoring meta-learning but lacks explicit contextual cues or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'validate' and 'upload' but does not disclose validation criteria, error behavior, or any safety or mutation details. The phrase 'charge-balanced biphasic' adds some behavioral context but is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action verbs, no unnecessary words. Every element contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 11 parameters and no output schema, the description is extremely brief. It omits return values, prerequisites (e.g., platform connection), side effects, and post-upload behavior. Given the tool's complexity, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 82% schema description coverage, the schema already documents most parameters. The description adds the conceptual context of a 'StimParam', but does not provide additional meaning beyond the schema's descriptions (e.g., units are already in schema).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs 'Define, validate & upload' and explicitly names the resource 'StimParam' with the qualifier 'charge-balanced biphasic stimulation'. This clearly distinguishes the tool from siblings like np_send_trigger or np_count_spikes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (configure stimulation parameters before sending triggers) but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure, but it only states the basic action without mentioning side effects, authorization needs, or whether repeated triggers are destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the purpose with no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 2 parameters and no output schema, the description covers the essential action. It lacks detail about system impact but is otherwise adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the input schema already documents both parameters. The description adds no additional meaning beyond the schema; baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Fire trigger(s)') and the resource ('uploaded StimParams via a 16-bit trigger array'), and distinguishes from sibling tools (e.g., np_status, np_configure_stim) by specifying the action on triggers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives (e.g., when to fire triggers vs configure stimuli), nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only report ('report'), but there are no annotations and no explicit statement about side effects or required state. For a reporting tool, the risk is low, but the description carries the full burden and lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 10 words, front-loading key information. It is extremely concise with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple report with no parameters and no output schema, the description names the metrics but does not explain their significance or the return format. Given the complex domain, a bit more context would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (empty schema). The description does not need to add parameter semantics. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it provides a 'Consciousness proxy report' listing specific metrics (GNW, Effective Information, Φ̃-RIIU, composite score). This clearly indicates what the tool does, but it does not differentiate from siblings like 'tcai_second_order' or 'tcai_metaconsciousness'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or exclusion criteria, which is a significant gap given the number of related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of disclosing behavior. It states that the tool resets multiple subsystems but does not indicate if the action is destructive, irreversible, or requires special permissions. For a reset operation, this omission is significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the verb 'Reset', and efficiently communicates the tool's purpose without extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of resetting a consciousness system with multiple components, the description is too brief. It lacks warnings about data loss, side effects, or usage context. An output schema is absent, so the description should provide more behavioral detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially covered. The description adds no parameter-specific meaning, but since no parameters exist, the baseline of 4 is appropriate. The description does not need to elaborate on inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resets the TCAI consciousness system, specifying four components (workspace, memory, emotion, metrics). The verb 'Reset' is specific, and the resource is well-defined, distinguishing it from siblings like 'snn_reset' (which resets a different subsystem) and 'tcai_workspace_state' (which reads state).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'snn_reset' or 'tcai_cycle'. There is no mention of prerequisites, consequences, or context for invocation. The description is purely a statement of action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description offers no behavioral details beyond the name. With no annotations, it fails to disclose side effects, idempotency, or requirements, leaving the agent with minimal insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and front-loaded, but it could be slightly more informative without losing conciseness. It earns its place but is minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description is adequate but lacks broader context about the latent space or when encoding is needed. It is minimally viable but leaves gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so baseline is 4. The description correctly implies no inputs needed, adding no extra meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Encode') and the resources involved ('SNN State' to 'Latent Space'), distinguishing it from sibling tools like wm_predict or wm_plan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It lacks context about prerequisites, conditions, or when encoding is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It describes the processing pipeline and the stopp condition, but lacks details on side effects (e.g., state mutation, data persistence, required permissions). Moderate disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loading the purpose and key parameter behavior. Every sentence adds value, though it could be slightly more terse. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 15 parameters, no output schema, and no annotations, the description should provide more context—e.g., return value, how cycles parameter interacts with stopp thresholds, or typical usage patterns. Adequate but misses significant details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 80%, so baseline is 3. The description adds context for stopWhenSatisfied and mentions it can halt early. Other parameters (threat, novelty, etc.) are not elaborated beyond the schema, so description adds limited additional meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs ACM cycles and enumerates the pipeline steps (SNN signals → AKOrN binding → ... → second-order loop). It distinguishes itself from sibling tools like tcai_emotion_appraise or tcai_self_model by being the overarching cycle runner.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions using stopWhenSatisfied to halt early, but does not explicitly guide when to use this tool versus individual sibling tools (e.g., when to run a full cycle vs. a single step). Usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits. The term 'snapshot' suggests a read-only operation, but the phrase 'observing and correcting its own predictive capacity' introduces ambiguity—whether the tool also performs corrections or only reports. No explicit statement of non-destructiveness or side effects is given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose with a list of components. It is concise, though the citation '(Legros 2026 §3.2)' may be non-actionable for an AI agent. Still, minimal waste overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description provides a conceptual summary of the snapshot's contents but does not specify the return format (e.g., JSON object, list) or how to interpret values. For a parameterless tool with no output schema, this is minimally adequate but leaves room for ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, and the schema description coverage is 100%. Per guidelines, baseline is 4 with 0 parameters since the schema already fully documents the input. The description adds no additional parameter meaning, which is acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a 'snapshot' of a 'second-order loop', listing specific components such as meta-learning velocity, RND curiosity, capability model, meta-consciousness score, and developmental stage. It distinguishes itself from sibling tools by combining these multiple meta-cognitive metrics into a single output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining a composite view of self-evidencing loop metrics but does not explicitly specify when to use this tool versus its sibling tools (e.g., tcai_meta_learning, tcai_curiosity). No when-to-use or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool returns a proxy (not a direct measurement) and lists the subcomponents. This adds meaningful behavioral context, though it omits potential side effects or idempotency guarantees.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose and adds clarifying nuance. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool, the description adequately explains the concept and hints at the return value (weighted score). However, it does not specify the score range or scale, which would help an agent interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and the description does not need to explain parameters. The baseline for 0-param tools is 4, and the description adds no irrelevant parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a weighted score over four specific components (confidence calibration, learning awareness, self-continuity, error monitoring) and clarifies it is a proxy, not a measurement. However, it lacks an explicit verb like 'computes' or 'retrieves', and does not differentiate from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as tcai_self_model or get_acm_score. The description does not specify prerequisites, context, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It enumerates returned quantities but does not state side effects, permissions, or cost. Being a data retrieval tool with 0 parameters, the risk is low, but the description could be more explicit about read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that packs significant detail without redundancy. However, it lacks paragraph structure or explicit front-loading of key actions. Appropriate for the technical content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description covers the main return values and cites relevant literature. It could mention typical use cases within the active inference loop or whether it is safe/side-effect-free, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; baseline 4. The description adds high value by explaining what the tool returns, compensating for missing output schema. It lists specific quantities that the schema (empty) does not convey.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns active-inference core telemetry including free energy, expected free energy, task quality, model entropy, and Dirichlet action. It distinguishes itself from siblings as the principled quantity for halting criterion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like tcai_metrics or tcai_curiosity. The description does not mention contexts, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It indicates a read-only query operation ('query', 'list'), but does not explicitly state non-destructive nature, permissions needed, or other side effects. Adequate but could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loading the key concept and then specifying the two operations. No redundant words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description hints at the return format ('expected-valence map', 'list...table'). It covers the two modes of operation adequately. Could benefit from more detail on data types, but it is complete enough for a simple query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one optional parameter. The description adds meaning by clarifying the two modes: providing an action queries its expected valence; omitting it lists the whole table. This goes beyond the schema's description of the parameter alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it queries an action's expected valence or lists the entire capability table. The verb 'query' and the resource 'capability model' are specific, and the tool is distinct from siblings like tcai_self_model or tcai_curiosity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: use when you need the expected outcome of an action or to see the learned table. It provides clear context but does not explicitly mention when not to use it or contrast with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the behavior: runs cycles, records |ΔF|, sets epsFreeEnergy, and returns measured scale and threshold. It discloses the side effect of setting an internal parameter. Could mention if this is safe during active inference, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and front-loaded. It states the primary goal, then method, outputs, and rationale in a logical order. Every sentence contributes value without redundancy. Ideal length for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions return values. Parameters are fully covered. However, it omits prerequisites (e.g., must a system be running?), error conditions, and potential side effects on ongoing processes. Slightly lacking in broader context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. The description adds context beyond schema by explaining how each parameter fits into the calibration process (e.g., 'reward signal during warm-up', 'epsFreeEnergy = factor × median|ΔF|'). This enhances understanding of parameter roles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: calibrating the halting threshold based on measured ΔF scale. It specifies the process (warm-up cycles, recording increments, setting threshold) and distinguishes it from the default constant approach. No sibling tool performs calibration, so it is well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for using this tool: when a calibrated threshold is needed instead of a guessed constant, referencing a specific critique. However, it does not explicitly state when to avoid using it or mention alternatives, though no such alternatives exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains the halting condition in detail, including the three criteria (free energy, task quality, epistemic value) and the patience cycles requirement. References external source for further details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: purpose, read vs write mode, and halting condition. Efficient and well-structured, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description indicates return of satisfaction state and active thresholds. Parameters are all optional and described. Complete for a read/write configuration tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 5 parameters with descriptions. The tool description adds collective context by explaining how parameters relate to the halting condition, but does not individually elaborate beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool inspects or configures the halting criterion, specifying that no arguments returns state and arguments update thresholds. This distinguishes it from sibling tools like tcai_cycle or tcai_workspace_state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes two usage modes: read (no arguments) and write (with arguments). Provides clear condition for each. However, lacks explicit guidance on when to use this over sibling tools like tcai_cycle or tcai_metrics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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